OpenRaiser/Intern-Atlas huggingface dataset:
Formalizing the Dependency Pair Criterion for Innermost Termination
Venue: arXiv cs.LO 2019
Year: 2019

Rewriting is a framework for reasoning about functional programming. The dependency pair criterion is a well-known mechanism to analyze termination of term rewriting systems. Functional specifications with an operational semantics based on evaluation are related, in the rewriting framework, to the innermost reduction relation. This paper presents a PVS formalization of the dependency pair criterion for the innermost reduction relation: a term rewriting system is innermost terminating if and only if it is terminating by the dependency pair criterion. The paper also discusses the application of this criterion to check termination of functional specifications.
A Graph-Based Tool to Embed the π-Calculus into a Computational DPO Framework
Venue: arXiv cs.LO 2019
Year: 2019

Graph transformation approaches have been successfully used to analyse and design chemical and biological systems. Here we build on top of a DPO framework, in which molecules are modelled as typed attributed graphs and chemical reactions are modelled as graph transformations. Edges and vertexes can be labelled with first-order terms, which can be used to encode, e.g., steric information of molecules. While targeted to chemical settings, the computational framework is intended to be very generic and applicable to the exploration of arbitrary spaces derived via iterative application of rewrite rules, such as process calculi like Milner's π-calculus. To illustrate the generality of the framework, we introduce EpiM: a tool for computing execution spaces of π-calculus processes. EpiM encodes π-calculus processes as typed attributed graphs and then exploits the existing DPO framework to compute their dynamics in the form of graphs where nodes are π-calculus processes and edges are reduction steps. EpiM takes advantage of the graph-based representation and facilities offered by the framework, like efficient isomorphism checking to prune the space without resorting to explicit structural equivalences. EpiM is available as an online Python-based tool.
Research and application of time series algorithms in centralized purchasing data
Venue: arXiv cs.AI 2019
Year: 2019

Based on the online transaction data of COSCO group's centralized procurement platform, this paper studies the clustering method of time series type data. The different methods of similarity calculation, different clustering methods with different K values are analysed, and the best clustering method suitable for centralized purchasing data is determined. The company list under the corresponding cluster is obtained. The time series motif discovery algorithm is used to model the centroid of each cluster. Through ARIMA method, we also made 12 periods of prediction for the centroid of each category. This paper constructs a matrix of "Customer Lifecycle Theory - Five Elements of Marketing ", and puts forward corresponding marketing suggestions for customers at different life cycle stages.
Review: Ordinary Differential Equations For Deep Learning
Venue: arXiv cs.LG 2019
Year: 2019

To better understand and improve the behavior of neural networks, a recent line of works bridged the connection between ordinary differential equations (ODEs) and deep neural networks (DNNs). The connections are made in two folds: (1) View DNN as ODE discretization; (2) View the training of DNN as solving an optimal control problem. The former connection motivates people either to design neural architectures based on ODE discretization schemes or to replace DNN by a continuous model characterized by ODEs. Several works demonstrated distinct advantages of using a continuous model instead of traditional DNN in some specific applications. The latter connection is inspiring. Based on Pontryagin's maximum principle, which is popular in the optimal control literature, some developed new optimization methods for training neural networks and some developed algorithms to train the infinite-deep continuous model with low memory-cost. This paper is organized as follows: In Section 2, the relation between neural architecture and ODE discretization is introduced. Some architectures are not motivated by ODE, but they are later found to be associated with some specific discretization schemes. Some architectures are designed based on ODE discretization and expected to achieve some special properties. Section 3 formulates the optimization problem where a traditional neural network is replaced by a continuous model (ODE). The formulated optimization problem is an optimal control problem. Therefore, two different types of controls will also be discussed in this section. In Section 4, we will discuss how we can utilize the optimization methods that are popular in optimal control literature to help the training of machine learning problems. Finally, two applications of using a continuous model will be shown in Section 5 and 6 to demonstrate some of its advantages over traditional neural networks.
SHACL Constraints with Inference Rules
Venue: arXiv cs.AI 2019
Year: 2019

The Shapes Constraint Language (SHACL) has been recently introduced as a W3C recommendation to define constraints that can be validated against RDF graphs. Interactions of SHACL with other Semantic Web technologies, such as ontologies or reasoners, is a matter of ongoing research. In this paper we study the interaction of a subset of SHACL with inference rules expressed in datalog. On the one hand, SHACL constraints can be used to define a "schema" for graph datasets. On the other hand, inference rules can lead to the discovery of new facts that do not match the original schema. Given a set of SHACL constraints and a set of datalog rules, we present a method to detect which constraints could be violated by the application of the inference rules on some graph instance of the schema, and update the original schema, i.e, the set of SHACL constraints, in order to capture the new facts that can be inferred. We provide theoretical and experimental results of the various components of our approach.
Computing Circle Packing Representations of Planar Graphs
Venue: arXiv cs.CG 2019
Year: 2019

The Circle Packing Theorem states that every planar graph can be represented as the tangency graph of a family of internally-disjoint circles. A well-known generalization is the Primal-Dual Circle Packing Theorem for 3-connected planar graphs. The existence of these representations has widespread applications in theoretical computer science and mathematics; however, the algorithmic aspect has received relatively little attention. In this work, we present an algorithm based on convex optimization for computing a primal-dual circle packing representation of maximal planar graphs, i.e. triangulations. This in turn gives an algorithm for computing a circle packing representation of any planar graph. Both take $\widetilde{O}(n \log(R/\varepsilon))$ expected run-time to produce a solution that is $\varepsilon$ close to a true representation, where $R$ is the ratio between the maximum and minimum circle radius in the true representation.
Automatic Calibration of Dual-LiDARs Using Two Poles Stickered with Retro-Reflective Tape
Venue: arXiv cs.RO 2019
Year: 2019

Multi-LiDAR systems have been prevalently applied in modern autonomous vehicles to render a broad view of the environments. The rapid development of 5G wireless technologies has brought a breakthrough for current cellular vehicle-to-everything (C-V2X) applications. Therefore, a novel localization and perception system in which multiple LiDARs are mounted around cities for autonomous vehicles has been proposed. However, the existing calibration methods require specific hard-to-move markers, ego-motion, or good initial values given by users. In this paper, we present a novel approach that enables automatic multi-LiDAR calibration using two poles stickered with retro-reflective tape. This method does not depend on prior environmental information, initial values of the extrinsic parameters, or movable platforms like a car. We analyze the LiDAR-pole model, verify the feasibility of the algorithm through simulation data, and present a simple method to measure the calibration errors w.r.t the ground truth. Experimental results demonstrate that our approach gains better flexibility and higher accuracy when compared with the state-of-the-art approach.
3D tissue reconstruction with Kinect to evaluate neck lymphedema
Venue: arXiv cs.CV 2019
Year: 2019

Lymphedema is a condition of localized tissue swelling caused by a damaged lymphatic system. Therapy to these tissues is applied manually. Some of the methods are lymph drainage, compression therapy or bandaging. However, the therapy methods are still insufficiently evaluated. Especially, because of not having a reliable method to measure the change of such a soft and flexible tissue. In this research, our goal has been providing a 3d computer vision based method to measure the changes of the neck tissues. To do so, we used Kinect as a depth sensor and built our algorithms for the point cloud data acquired from this sensor. The resulting 3D models of the patient necks are used for comparing the models in time and measuring the volumetric changes accurately. Our discussions with the medical doctors validate that, when used in practice this approach would be able to give better indication on which therapy method is helping and how the tissue is changing in time.
Anthropometric clothing measurements from 3D body scans
Venue: arXiv cs.CV 2019
Year: 2019

We propose a full processing pipeline to acquire anthropometric measurements from 3D measurements. The first stage of our pipeline is a commercial point cloud scanner. In the second stage, a pre-defined body model is fitted to the captured point cloud. We have generated one male and one female model from the SMPL library. The fitting process is based on non-rigid Iterative Closest Point (ICP) algorithm that minimizes overall energy of point distance and local stiffness energy terms. In the third stage, we measure multiple circumference paths on the fitted model surface and use a non-linear regressor to provide the final estimates of anthropometric measurements. We scanned 194 male and 181 female subjects and the proposed pipeline provides mean absolute errors from 2.5 mm to 16.0 mm depending on the anthropometric measurement.
Hybrid blind robust image watermarking technique based on DFT-DCT and Arnold transform
Venue: arXiv cs.MM 2019
Year: 2019

In this paper, a robust blind image watermarking method is proposed for copyright protection of digital images. This hybrid method relies on combining two well-known transforms that are the discrete Fourier transform (DFT) and the discrete cosine transform (DCT). The motivation behind this combination is to enhance the imperceptibility and the robustness. The imperceptibility requirement is achieved by using magnitudes of DFT coefficients while the robustness improvement is ensured by applying DCT to the DFT coefficients magnitude. The watermark is embedded by modifying the coefficients of the middle band of the DCT using a secret key. The security of the proposed method is enhanced by applying Arnold transform (AT) to the watermark before embedding. Experiments were conducted on natural and textured images. Results show that, compared with state-of-the-art methods, the proposed method is robust to a wide range of attacks while preserving high imperceptibility.
Variational Bayesian inference of hidden stochastic processes with unknown parameters
Venue: arXiv cs.LG 2019
Year: 2019

Estimating hidden processes from non-linear noisy observations is particularly difficult when the parameters of these processes are not known. This paper adopts a machine learning approach to devise variational Bayesian inference for such scenarios. In particular, a random process generated by the autoregressive moving average (ARMA) linear model is inferred from non-linearity noise observations. The posterior distribution of hidden states are approximated by a set of weighted particles generated by the sequential Monte carlo (SMC) algorithm involving sampling with importance sampling resampling (SISR). Numerical efficiency and estimation accuracy of the proposed inference method are evaluated by computer simulations. Furthermore, the proposed inference method is demonstrated on a practical problem of estimating the missing values in the gene expression time series assuming vector autoregressive (VAR) data model.
GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment
Venue: arXiv cs.IR 2019
Year: 2019

Effective biomedical literature retrieval (BLR) plays a central role in precision medicine informatics. In this paper, we propose GRAPHENE, which is a deep learning based framework for precise BLR. GRAPHENE consists of three main different modules 1) graph-augmented document representation learning; 2) query expansion and representation learning and 3) learning to rank biomedical articles. The graph-augmented document representation learning module constructs a document-concept graph containing biomedical concept nodes and document nodes so that global biomedical related concept from external knowledge source can be captured, which is further connected to a BiLSTM so both local and global topics can be explored. Query expansion and representation learning module expands the query with abbreviations and different names, and then builds a CNN-based model to convolve the expanded query and obtain a vector representation for each query. Learning to rank minimizes a ranking loss between biomedical articles with the query to learn the retrieval function. Experimental results on applying our system to TREC Precision Medicine track data are provided to demonstrate its effectiveness.
Sparse Regression Codes
Venue: arXiv cs.IT 2019
Year: 2019

Developing computationally-efficient codes that approach the Shannon-theoretic limits for communication and compression has long been one of the major goals of information and coding theory. There have been significant advances towards this goal in the last couple of decades, with the emergence of turbo codes, sparse-graph codes, and polar codes. These codes are designed primarily for discrete-alphabet channels and sources. For Gaussian channels and sources, where the alphabet is inherently continuous, Sparse Superposition Codes or Sparse Regression Codes (SPARCs) are a promising class of codes for achieving the Shannon limits. This survey provides a unified and comprehensive overview of sparse regression codes, covering theory, algorithms, and practical implementation aspects. The first part of the monograph focuses on SPARCs for AWGN channel coding, and the second part on SPARCs for lossy compression (with squared error distortion criterion). In the third part, SPARCs are used to construct codes for Gaussian multi-terminal channel and source coding models such as broadcast channels, multiple-access channels, and source and channel coding with side information. The survey concludes with a discussion of open problems and directions for future work.
A Streaming Analytics Language for Processing Cyber Data
Venue: arXiv cs.PL 2019
Year: 2019

We present a domain-specific language called SAL(the Streaming Analytics Language) for processing data in a semi-streaming model. In particular we examine the use case of processing netflow data in order to identify malicious actors within a network. Because of the large volume of data generated from networks, it is often only feasible to process the data with a single pass, utilizing a streaming (O(polylog n) space requirements) or semi-streaming computing model ( O(n polylog n) space requirements). Despite these constraints, we are able to achieve an average of 0.87 for the AUC of the ROC curve for a set of situations dealing with botnet detection. The implementation of an interpreter for SAL, which we call SAM (Streaming Analytics Machine), achieves scaling results that show improved throughput to 61 nodes (976 cores), with an overall rate of 373,000 netflows per second or 32.2 billion per day. SAL provides a succinct way to describe common analyses that allow cyber analysts to find data of interest, and SAM is a scalable interpreter of the language.
Generalized Learning with Rejection for Classification and Regression Problems
Venue: arXiv cs.LG 2019
Year: 2019

Learning with rejection (LWR) allows development of machine learning systems with the ability to discard low confidence decisions generated by a prediction model. That is, just like human experts, LWR allows machine models to abstain from generating a prediction when reliability of the prediction is expected to be low. Several frameworks for this learning with rejection have been proposed in the literature. However, most of them work for classification problems only and regression with rejection has not been studied in much detail. In this work, we present a neural framework for LWR based on a generalized meta-loss function that involves simultaneous training of two neural network models: a predictor model for generating predictions and a rejecter model for deciding whether the prediction should be accepted or rejected. The proposed framework can be used for classification as well as regression and other related machine learning tasks. We have demonstrated the applicability and effectiveness of the method on synthetically generated data as well as benchmark datasets from UCI machine learning repository for both classification and regression problems. Despite being simpler in implementation, the proposed scheme for learning with rejection has shown to perform at par or better than previously proposed methods. Furthermore, we have applied the method to the problem of hurricane intensity prediction from satellite imagery. Significant improvement in performance as compared to conventional supervised methods shows the effectiveness of the proposed scheme in real-world regression problems.
Testing noisy linear functions for sparsity
Venue: arXiv cs.CC 2019
Year: 2019

We consider the following basic inference problem: there is an unknown high-dimensional vector $w \in \mathbb{R}^n$, and an algorithm is given access to labeled pairs $(x,y)$ where $x \in \mathbb{R}^n$ is a measurement and $y = w \cdot x + \mathrm{noise}$. What is the complexity of deciding whether the target vector $w$ is (approximately) $k$-sparse? The recovery analogue of this problem --- given the promise that $w$ is sparse, find or approximate the vector $w$ --- is the famous sparse recovery problem, with a rich body of work in signal processing, statistics, and computer science. We study the decision version of this problem (i.e.~deciding whether the unknown $w$ is $k$-sparse) from the vantage point of property testing. Our focus is on answering the following high-level question: when is it possible to efficiently test whether the unknown target vector $w$ is sparse versus far-from-sparse using a number of samples which is completely independent of the dimension $n$? We consider the natural setting in which $x$ is drawn from a i.i.d.~product distribution $\mathcal{D}$ over $\mathbb{R}^n$ and the $\mathrm{noise}$ process is independent of the input $x$. As our main result, we give a general algorithm which solves the above-described testing problem using a number of samples which is completely independent of the ambient dimension $n$, as long as $\mathcal{D}$ is not a Gaussian. In fact, our algorithm is fully noise tolerant, in the sense that for an arbitrary $w$, it approximately computes the distance of $w$ to the closest $k$-sparse vector. To complement this algorithmic result, we show that weakening any of our condition makes it information-theoretically impossible for any algorithm to solve the testing problem with fewer than essentially $\log n$ samples.
Calcium Vulnerability Scanner (CVS): A Deeper Look
Venue: arXiv cs.CR 2019
Year: 2019

Traditional vulnerability scanning methods are time-consuming and indecisive, and they negatively affect network performance by generating high network traffic. In this paper, we present a novel vulnerability scanner that is time-efficient, simple, accurate, and safe. We call it a Calcium Vulnerability Scanner (CVS). Our contribution to vulnerability scanning are the following: (i) minimize its required time and network traffic: compared to current technologies, we reduced the former by an average of 79% and the latter by 99.9%, (ii) increase its accuracy: compared to current technologies, we improved this by an average of 2600%, and (iii) enable the scanner to learn from previous scans in order to reduce future scanning time and enhance accuracy: compared to current technologies, CVS reduced scanning time by an average of 97%. CVS enables a new frontier in vulnerability scanning and allow for scalable and efficient deployment of such tools in large-scale networks, containers, edge computing, and cloud computing.
SOK: A Comprehensive Reexamination of Phishing Research from the Security Perspective
Venue: arXiv cs.CR 2019
Year: 2019

Phishing and spear-phishing are typical examples of masquerade attacks since trust is built up through impersonation for the attack to succeed. Given the prevalence of these attacks, considerable research has been conducted on these problems along multiple dimensions. We reexamine the existing research on phishing and spear-phishing from the perspective of the unique needs of the security domain, which we call security challenges: real-time detection, active attacker, dataset quality and base-rate fallacy. We explain these challenges and then survey the existing phishing/spear phishing solutions in their light. This viewpoint consolidates the literature and illuminates several opportunities for improving existing solutions. We organize the existing literature based on detection techniques for different attack vectors (e.g., URLs, websites, emails) along with studies on user awareness. For detection techniques, we examine properties of the dataset, feature extraction, detection algorithms used, and performance evaluation metrics. This work can help guide the development of more effective defenses for phishing, spear-phishing, and email masquerade attacks of the future, as well as provide a framework for a thorough evaluation and comparison.
Generalized NLFSR Transformation Algorithms and Cryptanalysis of the Class of Espresso-like Stream Ciphers
Venue: arXiv cs.CR 2019
Year: 2019

Lightweight stream ciphers are highly demanded in IoT applications. In order to optimize the hardware performance, a new class of stream cipher has been proposed. The basic idea is to employ a single Galois NLFSR with maximum period to construct the cipher. As a representative design of this kind of stream ciphers, Espresso is based on a 256-bit Galois NLFSR initialized by a 128-bit key. The $2^{256}-1$ maximum period is assured because the Galois NLFSR is transformed from a maximum length LFSR. However, we propose a Galois-to-Fibonacci transformation algorithm and successfully transform the Galois NLFSR into a Fibonacci LFSR with a nonlinear output function. The transformed cipher is broken by the standard algebraic attack and the Rønjom-Helleseth attack with complexity $\mathcal{O}(2^{68.44})$ and $\mathcal{O}(2^{66.86})$ respectively. The transformation algorithm is derived from a new Fibonacci-to-Galois transformation algorithm we propose in this paper. Compare to existing algorithms, proposed algorithms are more efficient and cover more general use cases. Moreover, the transformation result shows that the Galois NLFSR used in any Espresso-like stream ciphers can be easily transformed back into the original Fibonacci LFSR. Therefore, this kind of design should be avoided in the future.